My analysis shows that a total of 248 mice were tested for Capomulin, Ceftamin, Infubinol, Ketapril, Naftisol, Placebo, Propriva, Ramicane, Stelasyn and Zoniferol. It was obvious that majority of the population groups were tested for Capomulin and Ramicane. In other plot, my findings also show that treatment through Capomulin has been effective.

A python based terminal interface to analyze the real-time performance of 3000+ company securities listed on NASDAQ. An extensive set of modules based on Pandas, NumPy, Matplotlib and Seaborn libraries. Also deployed the prediction of stock prices using Linear Regression.

Hotel Booking Demand EDA

Our World in Data's causes of death dataset is examined

Basic descriptive statistical analysis of LEAFF data

Data cleansing and handling missing data for a survey conducted by Stack Overflow

This repository contains project materials for the Winter STAT 206 class, University of California, Riverside, A. Gary Anderson School of Management.

Product Rationalization of Pro Bikes Inc using Power BI

Investigation of the evolutionary change in crimes in Chicago, IL.

Python with Tableau

Power BI based Data\Business analysis of e-commerce company with focus on demographic analysis

🐧🏠 This repository can be referred for basic data science analytics tasks such as descriptive analytics (data distributions & data preprocessing) and predictive modelling (classification or regression tasks).

Final Project: Predictive and descriptive analysis, interpretations, and recommendations based on Austin Animal Center data on cats housed at the shelter. The main focus of this analysis is to determine which factors affect a cat's chances of being adopted or returned to their owner.

Mercury NLQS (Natural Language Query System)

In order to create a good movie. We have chosen the score of 4.0 and above over 5.0. We will use the data to analyse what is needed to create a good movie.

Descriptive and predictive analytics on prescription volume for three outpatient pharmacies.

Web-based Clustering Application Using a Shiny Framework for Determining Student Engagement Levels in Virtual Learning Environments